This repository provides a comprehensive and hands-on guide to performing data analysis using the essential Python libraries: Pandas, Matplotlib, and Seaborn. It is designed for beginners and intermediate users looking to develop a robust workflow for exploring, cleaning, and visualizing data to extract meaningful insights.
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End-to-End Workflow: Follow a clear, step-by-step process for data analysis, from loading raw data to presenting final insights. Each project demonstrates a real-world application of the data analysis lifecycle.
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Data Manipulation with Pandas: Master the powerful DataFrame and Series data structures for efficient data wrangling. Learn how to: Load and clean datasets from various sources (CSV, Excel).
- Handle missing values using techniques like imputation and removal.
- Filter, sort, and group data to perform aggregate calculations.
- Merge and join multiple datasets for comprehensive analysis.
- Handle missing values using techniques like imputation and removal.
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Exploratory Data Analysis (EDA): Use descriptive statistics and visualizations to understand the structure and characteristics of your data. Discover patterns, trends, and anomalies before diving into a deeper analysis.
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Data Visualization with Matplotlib & Seaborn:
- Matplotlib: Create a wide range of static, animated, and interactive plots for foundational visualization tasks.
- Seaborn: Build on Matplotlib to generate aesthetically pleasing and statistically informative graphics with minimal code.
- Matplotlib: Create a wide range of static, animated, and interactive plots for foundational visualization tasks.
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Create common plots like bar charts, histograms, box plots, and heatmaps.